equation-discovery

FLARE is a forced latent autoencoder that discovers compact response coordinates and sparse input-dependent latent dynamics from high-dimensional observations of forced physical systems, enabling long-horizon forecasting under unseen inputs.

This paper introduces LLM-PySR, a method where language models guide symbolic equation discovery by controlling search parameters while using numerical symbolic regression for fitting. The approach achieves strong balance of accuracy and complexity across benchmark tasks.

PyCC.id is a Python library for hypothesis-driven equation discovery from time-series data, leveraging structural identifiability to help filter candidate models.